Health tracking device

ABSTRACT

A device may receive, from a plurality of sensors, sensor data relating to a user. The device may include a plurality of types of sensors including a spectrometer and one or more of an accelerometer, a heart rate sensor, a blood pressure sensor, a blood sugar sensor, a perspiration sensor, a skin conductivity sensor, or an imaging sensor. The device may process the sensor data, from the plurality of types of sensors, relating to the user to determine a health condition of the user. The device may provide, via a user interface, information identifying the health condition of the user based on processing the sensor data, from the plurality of types of sensors, relating to the user.

BACKGROUND

A wearable fitness tracker may perform a measurement of user activitybased on sensor data received from a sensor. For example, the wearablefitness tracker may include an accelerometer that provides sensor datato estimate user activity during an exercise session. The wearablefitness tracker may provide, for display, information associated withthe user activity during the exercise session. For example, the wearablefitness tracker may estimate one or more metrics, such as an estimateddistance traveled, an estimated calorie consumption metric, an estimatedmetabolic equivalents (METs) metric, or the like, based on the sensordata from the accelerometer and may provide the one or more metrics fordisplay.

SUMMARY

According to some possible implementations, a device may include one ormore processers. The one or more processors may receive, from aplurality of sensors, sensor data relating to a user. The plurality ofsensors may include a plurality of types of sensors including aspectrometer and one or more of an accelerometer, a heart rate sensor, ablood pressure sensor, a blood sugar sensor, a perspiration sensor, askin conductivity sensor, or an imaging sensor. The one or moreprocessors may process the sensor data, from the plurality of types ofsensors, relating to the user to determine a health condition of theuser. The one or more processors may provide, via a user interface,information identifying the health condition of the user based onprocessing the sensor data, from the plurality of types of sensors,relating to the user.

According to some possible implementations, a non-transitorycomputer-readable medium may store one or more instructions that, whenexecuted by one or more processors, may cause the one or more processorsto receive a first spectroscopic classification model. The firstspectroscopic classification model may be associated with identifying ahealth condition based on a chemometric signature. The firstspectroscopic classification model may be generated based on acalibration performed utilizing a spectrometer on a group of subjects.The one or more instructions, when executed by one or more processors,may cause the one or more processors to obtain a set of propertiesregarding a user. The set of properties including first sensor dataregarding the user. The one or more instructions, when executed by oneor more processors, may cause the one or more processors to generate asecond spectroscopic classification model based on the firstspectroscopic classification model and the set of properties regardingthe user. The second spectroscopic classification model may permit adetermination of a characteristic of the user or a food item. The one ormore instructions, when executed by one or more processors, may causethe one or more processors to periodically update the secondspectroscopic classification model based on second sensor data regardingthe user.

According to some possible implementations, a method may includedetermining, by a device, an activity level of a user based on firstsensor data relating to the activity level of the user from a first setof sensors. The method may include determining, by the device, anutritional content of a set of food items for consumption by the userbased on second sensor data relating to the nutritional content of theset of food items from a second set of sensors. The second sensor datamay be obtained from a spectrometer. The method may include obtaining,by the device, a stored diet and exercise plan for the user. The storeddiet and exercise plan may include a goal relating to the activity levelof the user and the nutritional content of the set of food items forconsumption by the user. The method may include determining, by thedevice, a user compliance with the stored diet and exercise plan basedon the activity level of the user and the nutritional content of the setof food items. The method may include providing, by the device, arecommendation relating to user compliance with the stored diet andexercise plan based on determining the user compliance with the storeddiet and exercise plan.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of an overview of an example implementationdescribed herein;

FIG. 2 is a diagram of an example environment in which systems and/ormethods, described herein, may be implemented;

FIG. 3 is a diagram of example components of one or more devices of FIG.2;

FIG. 4 is a flow chart of an example process for providing healthinformation based on sensor data associated with multiple sensors;

FIGS. 5A-5D are diagrams of an example implementation relating to theexample process shown in FIG. 4;

FIG. 6 is a flow chart of an example process for dynamically updating aclassification model for spectroscopic analysis; and

FIGS. 7A and 7B are diagrams of an example implementation relating tothe example process shown in FIG. 6.

DETAILED DESCRIPTION

The following detailed description of example implementations refers tothe accompanying drawings. The same reference numbers in differentdrawings may identify the same or similar elements.

A wearable fitness tracker may utilize a set of accelerometers to obtainsensor data regarding user activity. For example, the wearable fitnesstracker may determine an estimated distance traveled by a user based onsensor data identifying a movement of the wearable fitness tracker. Thewearable fitness tracker may include information that may be utilized toestimate one or more metrics relating to user activity. For example, thewearable fitness tracker may estimate a calorie expenditure based on theestimated distance traveled and generic information correlating distancetraveled to calorie expenditure for a generic person.

However, utilizing generic correlations to determine the one or moremetrics may result in an inaccurate calculation for a particular user.Moreover, the wearable fitness tracker may fail to account for factors,other than exercise, that affect health of a user, such as nutrition,mood, illness, or the like, thereby limiting a usefulness of thewearable fitness tracker. Implementations, described herein, may utilizesensor data associated with multiple sensors to provide healthinformation associated with a user. In this way, a single user devicemay determine the health information for a user, obviating the need forthe user to utilize multiple different devices, thereby reducing cost,power consumption, or the like relative to utilizing the multipledifferent devices for health tracking. Moreover, the user device mayutilize the sensor data from the multiple sensors to facilitatecalibration of a model for spectroscopic analysis, thereby improvingaccuracy of the model relative to utilizing a model that is notcalibrated based on sensor data from other sensors.

FIG. 1 is a diagram of an overview of an example implementation 100described herein. As shown in FIG. 1, example implementation 100includes a user device, a health care provider application server, and aspectroscopy calibration application server.

As further shown in FIG. 1, the user device may receive sensor data frommultiple sensors (e.g., first sensor data from a first sensor, secondsensor data from a second sensor, . . . , and nth sensor data from annth sensor). For example, the user device 210 may utilize a set ofintegrated sensors to obtain the sensor data, such as an integratedaccelerometer sensor to obtain user activity data, an integrated heartrate sensor to obtain heart rate data, an integrated temperature sensorto obtain temperature data, or the like. Additionally, or alternatively,the user device may utilize a camera to obtain the sensor data. Forexample, the user device may utilize an integrated camera to capture animage, such as an image of a user's face (e.g., for a facial recognitionanalysis), an image of the user's skin (e.g., for a skin conditionanalysis), a set of images of food (e.g., a set of images for performinga volumetric analysis of the food), or the like. Additionally, oralternatively, the user device may utilize a spectroscopic sensor toobtain the sensor data. For example, the user device may utilize thespectroscopic sensor to determine a chemometric signature for a subject(e.g., the user or an item of food), and may classify the subject basedon the chemometric signature and a classification model. In anotherexample, the user device may communicate with one or more sensors toobtain sensor data. For example, the user device may utilize aconnection to a medical device to obtain sensor data recorded by themedical device.

As further shown in FIG. 1, the user device may combine the sensor datafrom the multiple sensors to generate health information regarding theuser. For example, based on sensor data regarding a user activity level,a user heart rate, a user body temperature, or the like and storedinformation regarding a user height, a user weight, or the like, theuser device may determine a calorie expenditure by the user associatedwith an exercise program. Based on utilizing information in addition toaccelerometer data (e.g., the user heart rate data and/or the user bodytemperature data), the user device may obtain a more accuratedetermination of the calorie expenditure than based on only utilizingthe accelerometer data.

Similarly, based on image sensor data and spectroscopic sensor data, theuser device may determine, respectively, a volume of a food item and anutritional content of the food item. Based on utilizing thespectroscopic sensor data identifying a composition of the food item andthe sensor data identifying a volume of the food item, the user devicemay obtain a more accurate determination of calorie intake by the userthan based on utilizing a user estimation of nutritional value. Based onthe calorie expenditure determination and the calorie intakedetermination, the user device may determine a net calorie consumptionfor a user, and may generate a recommendation associated with the netcalorie consumption, such as a nutritional recommendation, an exerciserecommendation, or the like to improve user health and/or usercompliance with a diet and exercise plan. In this way, the user deviceperforms determinations relating to health of the user that are moreaccurate than determinations performed by a wearable fitness tracker,thereby permitting improved fitness tracking.

As another example, the user device may process the sensor data todetermine diagnostic information regarding a user. For example, based onperforming a pattern recognition analysis on an image of the user, theuser device may detect facial redness and pimples associated with arosacea condition. Similarly, based on performing a pattern recognitionanalysis on an image of the user, the user device may determine that theuser is in a comfort state, and may correlate the comfort state withother sensor data, such as sensor data indicating that the user waspreviously eating a particular item of food. In this case, the userdevice may periodically provide a recommendation relating to altering amood of the user, such as a recommendation that the user eat theparticular item of food that correlates with a comfort state.

As another example, the user device may process spectroscopic sensordata to calibrate a classification model for spectroscopy. For example,the user device may utilize a first model to perform a firstclassification of first spectroscopic sensor data, such as to classifyan observed chemometric signature as relating to a particular personwith a particular blood pressure and after eating a particular fooditem. In this case, the user device may calibrate the first model togenerate a second model based on the first spectroscopic sensor data andother sensor data (e.g., sensor data identifying the particular bloodpressure and the particular food item), and may utilize the second modelto perform another classification of second spectroscopic sensor data.For example, the user device may utilize the second classification modelto distinguish between the particular person when the particular personis associated with the particular blood pressure and the particularperson when the particular person is associated with another bloodpressure. In this way, the user device refines a classification model toperform spectroscopy with improved accuracy relative to a classificationmodel that is not refined based on other sensor data.

As further shown in FIG. 1, the user device may provide information,such as health information relating to the user, diagnostic informationrelating to a health condition of the user, recommendations relating toimproving user health and/or compliance with a diet and exercise plan,or the like based on processing the sensor data from the multiplesensors. Compliance with a diet and exercise plan may be associated withreducing a severity of a health condition (e.g., a heart diseasecondition or an obesity condition), managing symptoms of a healthcondition (e.g., a diabetes health condition or a stress condition),reducing a likelihood of deterioration of a health condition (e.g., adegenerative condition), achieving a desired health condition (e.g.,improving diet, increasing an intake of whole grains, increasing musclemass, or improving an athletic performance). For example, the userdevice may provide, for display via a user interface, a real-time healthupdate, such as information indicating a net calorie consumption basedon the calorie expenditure and the calorie intake determinations.Additionally, or alternatively, the user device may provide diagnosticinformation indicating that a particular condition is detected for theuser, and may automatically transmit data to a specialist to establishan appointment for the user with the specialist. Additionally, oralternatively, the user device may generate a recommendation, such as arecommendation for an exercise regimen to permit the user to comply witha diet and exercise plan, and may provide updates regarding usercompliance with the diet and exercise plan for display via anotherdevice (e.g., a user device utilized by a personal trainer or anutritionist). In this way, the user device provides informationcustomized to the user rather than generic information.

As further shown in FIG. 1, the user device may provide healthinformation to a health care provider application server for inclusionin a patient file relating to the user. In this way, the user deviceimproves doctor patient consultations by improving data accuracyrelative to a doctor relying on manual patient reporting of diet,exercise, or the like and reducing a utilization of processing resourcesand/or a power consumption relative to providing one or more userinterfaces for receiving input of patient reporting and/or correctingthe input of the patient reporting. Additionally, or alternatively, theuser device may provide calibration data to spectroscopy calibrationapplication server. For example, the user device may provide calibrationinformation determined from a spectroscopic sensor and/or other sensorsfor utilization in calibrating and/or refining a classification modelthat is provided to one or more other user devices, thereby improvingaccuracy of spectroscopy for the one or more other user devices relativeto the one or more other user devices receiving a model not calibratedbased on the calibration information.

In this way, the user device provides health information determinedbased on sensor data obtained from multiple sensors, thereby reducingcost and power consumption relative to the user utilizing multipledifferent devices. Moreover, based on integrating the sensor datacollection and processing into a single user device, the user devicepermits determination of health information that is not obtainable via asingle sensor, such as obtaining nutrition information based on both avolumetric analysis and a spectroscopic analysis or the like.Furthermore, the user device improves calibration of a classificationmodel for spectroscopic analysis based on performing model calibrationand model refinement based on both spectroscopic data and other sensordata regarding a subject of spectroscopic analysis relative to a singleinitial calibration via a single device.

As indicated above, FIG. 1 is provided merely as an example. Otherexamples are possible and may differ from what was described with regardto FIG. 1.

FIG. 2 is a diagram of an example environment 200 in which systemsand/or methods, described herein, may be implemented. As shown in FIG.2, environment 200 may include a user device 210, an application server220, and a network 230. Devices of environment 200 may interconnect viawired connections, wireless connections, or a combination of wired andwireless connections.

User device 210 includes one or more devices capable of receiving,generating, storing, processing, and/or providing health information.For example, user device 210 may include a communication and/orcomputing device, such as a mobile phone (e.g., a smart phone, aradiotelephone, etc.), a laptop computer, a tablet computer, a handheldcomputer, a gaming device, a wearable device (e.g., a smart wristwatch,a pair of smart eyeglasses, a smart wristband, etc.), a medical device,a spectroscopic device (e.g., a wearable spectrometer device thatperforms near infrared (NIR) spectroscopy, mid-infrared (mid-IR)spectroscopy, or Raman spectroscopy), or a similar type of device. Insome implementations, the spectroscopic device may include ahyperspectral spectrometer (e.g., a hyperspectral imaging sensor). Insome implementations, user device 210 may receive information fromand/or transmit information to another device in environment 200.

Application server 220 includes one or more devices capable of storing,processing, and/or routing information, such as health information,calibration information, or the like. For example, application server220 may include a server that utilizes health information and/orinformation associated with user device 210. In some implementations,application server 220 may include a calibration application server 220that receives information associated with calibration of a spectroscopicmodel based on a set of measurements performed by one or more userdevices 210. Additionally, or alternatively, application server 220 mayinclude a health care provider application server 220 associated withrouting information for a health care provider, such as a hospitalserver or the like. In some implementations, application server 220 mayreceive information from and/or transmit information to another devicein environment 200.

Network 230 includes one or more wired and/or wireless networks. Forexample, network 230 may include a cellular network (e.g., a long-termevolution (LTE) network, a 3G network, a code division multiple access(CDMA) network, etc.), a public land mobile network (PLMN), a local areanetwork (LAN), a wide area network (WAN), a metropolitan area network(MAN), a telephone network (e.g., the Public Switched Telephone Network(PSTN)), a private network, an ad hoc network, an intranet, theInternet, a fiber optic-based network, a cloud computing network, or thelike, and/or a combination of these or other types of networks.

The number and arrangement of devices and networks shown in FIG. 2 areprovided as an example. In practice, there may be additional devicesand/or networks, fewer devices and/or networks, different devices and/ornetworks, or differently arranged devices and/or networks than thoseshown in FIG. 2. Furthermore, two or more devices shown in FIG. 2 may beimplemented within a single device, or a single device shown in FIG. 2may be implemented as multiple, distributed devices. Additionally, oralternatively, a set of devices (e.g., one or more devices) ofenvironment 200 may perform one or more functions described as beingperformed by another set of devices of environment 200.

FIG. 3 is a diagram of example components of a device 300. Device 300may correspond to user device 210 and/or application server 220. In someimplementations, user device 210 and/or application server 220 mayinclude one or more devices 300 and/or one or more components of device300. As shown in FIG. 3, device 300 may include a bus 310, a processor320, a memory 330, a storage component 340, an input component 350, anoutput component 360, and a communication interface 370.

Bus 310 includes a component that permits communication among thecomponents of device 300. Processor 320 is implemented in hardware,firmware, or a combination of hardware and software. Processor 320 mayinclude a processor (e.g., a central processing unit (CPU), a graphicsprocessing unit (GPU), an accelerated processing unit (APU), etc.), amicroprocessor, and/or any processing component (e.g., afield-programmable gate array (FPGA), an application-specific integratedcircuit (ASIC), etc.) that interprets and/or executes instructions. Insome implementations, processor 320 may include one or more processorscapable of being programmed to perform a function. Memory 330 includes arandom access memory (RAM), a read only memory (ROM), and/or anothertype of dynamic or static storage device (e.g., a flash memory, amagnetic memory, an optical memory, etc.) that stores information and/orinstructions for use by processor 320.

Storage component 340 stores information and/or software related to theoperation and use of device 300. For example, storage component 340 mayinclude a hard disk (e.g., a magnetic disk, an optical disk, amagneto-optic disk, a solid state disk, etc.), a compact disc (CD), adigital versatile disc (DVD), a floppy disk, a cartridge, a magnetictape, and/or another type of non-transitory computer-readable medium,along with a corresponding drive.

Input component 350 includes a component that permits device 300 toreceive information, such as via user input (e.g., a touch screendisplay, a keyboard, a keypad, a mouse, a button, a switch, amicrophone, etc.). Additionally, or alternatively, input component 350may include a sensor for sensing information (e.g., a global positioningsystem (GPS) component, an accelerometer, a gyroscope, an actuator,etc.). Output component 360 includes a component that provides outputinformation from device 300 (e.g., a display, a speaker, one or morelight-emitting diodes (LEDs), etc.).

Communication interface 370 includes a transceiver-like component (e.g.,a transceiver, a separate receiver and transmitter, etc.) that enablesdevice 300 to communicate with other devices, such as via a wiredconnection, a wireless connection, or a combination of wired andwireless connections. Communication interface 370 may permit device 300to receive information from another device and/or provide information toanother device. For example, communication interface 370 may include anEthernet interface, an optical interface, a coaxial interface, aninfrared interface, a radio frequency (RF) interface, a universal serialbus (USB) interface, a Wi-Fi interface, a cellular network interface, orthe like.

Device 300 may perform one or more processes described herein. Device300 may perform these processes in response to processor 320 executingsoftware instructions stored by a non-transitory computer-readablemedium, such as memory 330 and/or storage component 340. Acomputer-readable medium is defined herein as a non-transitory memorydevice. A memory device includes memory space within a single physicalstorage device or memory space spread across multiple physical storagedevices.

Software instructions may be read into memory 330 and/or storagecomponent 340 from another computer-readable medium or from anotherdevice via communication interface 370. When executed, softwareinstructions stored in memory 330 and/or storage component 340 may causeprocessor 320 to perform one or more processes described herein.Additionally, or alternatively, hardwired circuitry may be used in placeof or in combination with software instructions to perform one or moreprocesses described herein. Thus, implementations described herein arenot limited to any specific combination of hardware circuitry andsoftware.

The number and arrangement of components shown in FIG. 3 are provided asan example. In practice, device 300 may include additional components,fewer components, different components, or differently arrangedcomponents than those shown in FIG. 3. Additionally, or alternatively, aset of components (e.g., one or more components) of device 300 mayperform one or more functions described as being performed by anotherset of components of device 300.

FIG. 4 is a flow chart of an example process 400 for providing healthinformation based on sensor data associated with multiple sensors. Insome implementations, one or more process blocks of FIG. 4 may beperformed by user device 210. In some implementations, one or moreprocess blocks of FIG. 4 may be performed by another device or a groupof devices separate from or including user device 210, such asapplication server 220 or the like.

As shown in FIG. 4, process 400 may include obtaining sensor data from aset of sensors (block 410). For example, user device 210 may obtainsensor data from the set of sensors of user device 210. In someimplementations, user device 210 may obtain the sensor data based oncommunicating with the set of sensors. For example, user device 210 maycause a heartbeat sensor of user device 210 to activate and recordsensor data regarding a heartbeat of a user. Similarly, user device 210may cause an accelerometer sensor of user device 210 to activate andrecord accelerometer sensor data regarding movement of user device 210(e.g., activity of a user using user device 210).

In some implementations, user device 210 may obtain the sensor databased on providing a prompt via a user interface. For example, userdevice 210 may provide a prompt to cause a user to utilize user device210 to capture an image of the user (e.g., a photograph of the user'sface or a photograph of a portion of the user's skin). Additionally, oralternatively, user device 210 may provide a prompt to cause the user toutilize user device 210 to capture an image of an item of food. Forexample, user device 210 may be utilized to capture an image of a meal,and may process the image of the meal to identify a nutritional contentof the meal. Additionally, or alternatively, user device 210 may capturean image automatically. For example, user device 210 may determine thata meal is prepared, such as based on a time of day, based on locationdata indicating that user device 210 is at a restaurant, based on socialmedia information indicating that user device 210 is at a restaurant, orthe like, and may automatically activate an imaging sensor to capture animage. Similarly, user device 210 may determine, based on anaccelerometer, based on a touch sensor, or the like, that an imagingsensor of user device 210 is directed toward the user, and may cause theimaging sensor to capture an image of the user.

In some implementations, user device 210 may monitor an imaging sensorto obtain sensor data. For example, user device 210 may perform anobject recognition technique on a set of images captured via the imagingsensor to determine whether the set of images captured includeinformation utilizable for providing a health report, such asdetermining that a particular image includes a food item, determiningthat the particular image includes the user, or the like. In this case,user device 210 may select the particular image for processing (e.g., avolumetric analysis processing technique, a mood analysis processingtechnique, or a skin condition processing technique).

In some implementations, user device 210 may obtain the sensor databased on a trigger. For example, user device 210 may detect a userinteraction with a user interface, and may be caused to obtain thesensor data. Additionally, or alternatively, user device 210 may obtainthe sensor data periodically. For example, based on determining that athreshold period of time has elapsed (e.g., an hour, a day, or a week),user device 210 may obtain the sensor data. Similarly, at a particulartime of day, user device 210 may obtain the sensor data. In someimplementations, user device 210 may obtain the sensor data based onother sensor data. For example, user device 210 may detect that a userhas finished an exercise regimen based on monitoring a heart rate sensor(e.g., based on heart rate sensor data), and may obtain other sensordata based on determining that the user has finished the exerciseregimen. Similarly, user device 210 may identify the user based onchemometric signature sensor data from a spectrometer sensor, and mayobtain other sensor data based on identifying the user.

As further shown in FIG. 4, process 400 may include processing thesensor data to obtain health information (block 420). For example, userdevice 210 may process the sensor data to obtain the health information.In some implementations, user device 210 may process the sensor data toidentify one or more metrics related to user health. For example, userdevice 210 may determine an activity level metric based on sensor dataassociated with multiple types of sensors, such as an accelerometer, aheart rate sensor, a skin temperature sensor, a blood pressure sensor,or the like. Similarly, user device 210 may determine a nutrition metricbased on sensor data associated with a blood pressure sensor, a bloodsugar sensor, a perspiration sensor, a skin conductivity sensor, or thelike.

In some implementations, user device 210 may utilize a particularprocessing technique to process the sensor data. For example, userdevice 210 may utilize a classification technique associated with aclassification model to identify content of a food item based on aspectroscopic measurement (e.g., a chemometric signature), and maydetermine health information related to food consumption based onidentifying the content of the food item. In some implementations, userdevice 210 may perform the classification based on sensor data receivedfrom a hyperspectral spectrometer, which may user difficulty and errorsrelated to distance from the subject relative to utilizing another typeof sensor. Additionally, or alternatively, user device 210 may apply apattern detection technique, a facial recognition technique, athree-dimensional depth sensing technique, or the like to an image of auser to determine a user mood, a level of fatigue, a level of stress, amigraine symptom, a skin condition, or the like. Additionally, oralternatively, user device 210 may utilize a color classificationtechnique to determine that a color of urine in an image corresponds toa particular health condition, such as insufficient or excessiveconsumption of a particular vitamin (e.g., a B-vitamin deficiency) orthe like.

Additionally, or alternatively, user device 210 may utilize a volumetricanalysis technique to process the sensor data. For example, user device210 may utilize a set of images of a food item (e.g., a set of imagescaptured from different orientations and/or positions), to determine avolume of the food item. Additionally, or alternatively, user device 210may utilize sensor data captured by a depth sensing module, a gesturerecognition module, or the like to perform the volumetric analysis. Insome implementations, user device 210 may determine a food item massbased on the volumetric analysis. For example, user device 210 mayutilize information identifying a density of the food item (e.g., sensordata, image recognition of the food item, or a user selection of thefood item and corresponding stored food item density data), and maydetermine a mass for the food based on the density (e.g., which may beutilized to determine a nutritional content of the food based on aspectroscopic analysis indicating a nutritional content on a per massbasis). Additionally, or alternatively, user device 210 may utilize aset of images of a user to determine a volume of a portion of the user,such as a volume of a shoulder hump, a skin bump, or the like. In thisway, user device 210 identifies a volume of a subject for comparisonwith the subject at another time, to determine a nutritional content ofthe subject (e.g., with other sensor data identifying a type of fooditem that is the subject), or the like.

In some implementations, user device 210 may process the sensor datausing a comparison technique, such as comparing first sensor datarecorded at a first time to second sensor data recorded at a secondtime. For example, user device 210 may compare a first three-dimensionalimage of a user at a first time period with a second three-dimensionalimage of the user at a second time period to identify changes to theuser's appearance, such as growth of a mole (e.g., corresponding totumor growth), change to a breast shape (e.g., corresponding to cystgrowth), change to a body shape (e.g., corresponding to a weight gain),change to a chemometric signature (e.g., corresponding to a change inblood composition associated with a diabetes type disorder), or thelike.

As further shown in FIG. 4, process 400 may include providing the healthinformation (block 430). For example, user device 210 may provide thehealth information. In some implementations, user device 210 mayprovide, via a user interface, a health report for review by a user. Forexample, user device 210 may provide a report including healthinformation identifying a net calorie consumption (e.g., a comparison ofcalorie intake and calorie expenditure) for the user. Additionally, oralternatively, user device 210 may provide information identifying aportion of sensor data processed by user device 210, such as a set ofvital statistics determined for the user (e.g., a blood pressure, a bodytemperature, a heart rate, or a perspiration level).

In some implementations, user device 210 may provide a recommendationbased on the sensor data. For example, user device 210 may determine aset of health recommendations (e.g., to improve an obesity condition, tomanage a diabetes condition, to prevent a degenerative condition, toimprove an athletic performance, or to satisfy a nutrition goal), mayselect a particular health recommendation of the set of healthrecommendations (e.g., that the user consume a particular quantity ofcalories during a particular meal based on a metabolic rate of theuser), and may provide health information including the particularhealth recommendation for display via a user interface. Additionally, oralternatively, user device 210 may select another health recommendation(e.g., that the user is predicted to experience improved mood levelsbased on consuming a particular food or engaging in a particularexercise regimen), and may provide the other health recommendation fordisplay via another user device 210 (e.g., that is utilized by aphysician, a life coach, a trainer, or a personal chef).

In some implementations, user device 210 may provide an alert based onthe sensor data. For example, user device 210 may provide, based onsensor data identifying a health condition of the user, a particularalert identifying the health condition for display via the userinterface. In this case, the alert may identify the health condition, aseverity of the health condition, or the like. Additionally, oralternatively, user device 210 may provide an alert for display viaanother user device 210. For example, user device 210 may identify aspecialist doctor associated with the health condition, and may transmitan alert for display via another user device 210 that is utilized by thespecialist doctor. Similarly, user device 210 may transmit an alert tocause emergency management personnel to be dispatched for the user. Forexample, when user device 210 determines that a severity of the healthcondition satisfies a threshold severity, user device 210 may utilize alocation determination technique to determine a location of the user,and may transmit an alert to an ambulance dispatch, a hospital, or thelike to cause emergency management personnel to be dispatched to thelocation of the user. In some implementations, user device 210 mayprovide a health report when providing the alert. For example, userdevice 210 may provide information identifying a blood pressure of theuser, a heart rate of the user, a body temperature of the user, or thelike for utilization by a doctor, by emergency management personnel, orthe like.

In some implementations, user device 210 may provide calibration data.For example, when user device 210 transmits spectroscopic data regardinga user for calibrating a model, user device 210 may provide healthinformation regarding the user for calibrating the model. In this case,the health information may be utilized to calibrate the model to aparticular health condition (e.g., a first chemometric signature may bedetermined to correspond to a first perspiration level and a secondchemometric signature may be determined to correspond to a secondperspiration level).

In some implementations, user device 210 may provide, for display, anaugmented reality image based on the sensor data. For example, when userdevice 210 determines a caloric content of a food item based on imagesof the food item, user device 210 may determine a portion of the fooditem that, when consumed, corresponds to satisfying a nutrition goal. Inthis case, user device 210 may provide, for display, an image of thefood item with the portion of the food item highlighted, an augmentedreality display of the food item with the portion of the food itemhighlighted, or the like. In this way, a user may be providedinformation indicating how much of a food item to consume to satisfy anutrition goal.

Although FIG. 4 shows example blocks of process 400, in someimplementations, process 400 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 4. Additionally, or alternatively, two or more of theblocks of process 400 may be performed in parallel.

FIGS. 5A-5D are diagrams of an example implementation 500 relating toexample process 400 shown in FIG. 4. FIGS. 5A-5D show an example ofproviding health information based on sensor data associated withmultiple sensors.

As shown in FIG. 5A, and by reference number 510, a wearable user device210 (e.g., a smart watch including a set of sensors) may store a set ofdiet and exercise goals received from a health care provider applicationserver 220. For example, the set of diet and exercise goals may begenerated based on a user consultation with a health care provider(e.g., a doctor, a personal trainer, or a nutritionist), and may betransmitted to wearable user device 210 to permit wearable user device210 to monitor user diet and exercise to determine and improvecompliance with the set of diet and exercise goals. Assume that the setof diet and exercise goals includes a diet goal associated with the userhaving an intake of a threshold amount of carbohydrates in a day, anexercise goal associated with the user satisfying a threshold physicalactivity level during the day, and a combined diet and exercise goalassociated with the user satisfying a threshold net calorie consumption(e.g., a greater calorie expenditure than calorie intake during theday).

As shown in FIG. 5B, and by reference number 515, wearable user device210 performs a spectroscopic analysis of a food item to determine acontent of the food item, such as a protein content, a fat content, or acarbohydrate content using data from a spectroscopic sensor. As shown byreference number 520, wearable user device 210 performs an imageanalysis of food item to determine a volume of the food item using a setof images from an imaging sensor. Assume that wearable user device 210determines a carbohydrate intake and a calorie intake for the user basedon the content of the food item (e.g., the spectroscopic analysisindicating the content of the food) and the volume of the food item(e.g., the volumetric analysis indicating the content of the food).Assume that based on the carbohydrate intake, wearable user device 210provides information via the user interface indicating compliance withthe diet goal, such as an alert indicating an amount of carbohydratesthat the user is permitted to consume during the remainder of the day.

As shown in FIG. 5C, and by reference number 525, wearable user device215 performs an activity analysis of a user exercise regimen using datafrom a heart rate sensor, an accelerometer sensor, or the like. Assumethat wearable user device 210 determines a calorie expenditure and aphysical activity level based on performing the activity analysis of theuser exercise regimen. Assume that based on the physical activity level,wearable user device 210 provides information via the user interfaceindicating compliance with the exercise goal, such as informationindicating that the user satisfied the threshold physical activity levelfor the day.

As shown in FIG. 5D, and by reference number 530, wearable user device210 determines compliance with the combined diet and exercise goal andgenerates an activity plan for the user associated with causing the userto perform a particular activity (e.g., a particular quantity of steps)associated with a corresponding alteration to sensor data associatedwith the user (e.g., causing the sensor data to indicate that usercalorie expenditure has increased for the day). Assume that wearableuser device 210 determines that the user has failed to satisfy thecombined diet and exercise goal based on the calorie intake for the userexceeding the calorie expenditure by the user. Further assume thatwearable user device 210 determines that based on previous activity bythe user, walking a particular quantity of steps by the user before theend of the day corresponds to achieving the combined diet and exercisegoal. As shown by reference number 535, wearable user device 210provides an alert indicating that the user is to walk the particularquantity of steps before the end of the day, and continues to monitoruser activity and provide alerts to cause the user to walk theparticular quantity of steps. In this way, wearable user device 210improves compliance with the combined diet and exercise goal relative toa step counter device that only provides information indicating stepstake and estimated calorie expenditure without including other data andwithout comparing estimated calorie expenditure to calorie intake.

As indicated above, FIGS. 5A-5D are provided merely as an example. Otherexamples are possible and may differ from what was described with regardto FIGS. 5A-5D.

FIG. 6 is a flow chart of an example process 600 for dynamicallyupdating a classification model for spectroscopic analysis. In someimplementations, one or more process blocks of FIG. 6 may be performedby user device 210. In some implementations, one or more process blocksof FIG. 6 may be performed by another device or a group of devicesseparate from or including user device 210, such as application server220.

As shown in FIG. 6, process 600 may include obtaining a firstclassification model for spectroscopic analysis (block 610). Forexample, user device 210 may obtain the first classification model forspectroscopic analysis. A classification model (e.g., a spectroscopicclassification model) may refer to a model that may be utilized toidentify a subject of spectroscopic analysis or a characteristic of thesubject of spectroscopic analysis based on a chemometric signatureobtained for the subject. For example, the classification model mayinclude information associated with a set of chemometric signatures fora set of samples (e.g., a set of persons or a set of food items), anduser device 210 may utilize the classification model to determine that achemometric signature for a person corresponds to a particularcharacteristic (e.g., a blood glucose level) for the person. Similarly,user device 210 may utilize another classification model to determinethat a chemometric signature for a food item corresponds to a particularnutritional content for the food item.

In some implementations, user device 210 may obtain the firstclassification model from a particular application server 220 associatedwith calibrating the first classification model. For example, theparticular application server 220 may perform spectroscopic analysis ona calibration set (e.g., a set of identified subjects) using aspectrometer, and may utilize a processing technique (e.g., anoptimization technique to distinguish between respective chemometricsignatures for the calibration set) to generate the first classificationmodel. In this case, user device 210 may receive the firstclassification model based on the calibration model being optimized byapplication server 220. In some implementations, user device 210 mayobtain a particular classification model that is calibrated based onspectroscopic analysis performed on a set of persons. For example,application server 220 may perform a first spectroscopic analysis on afirst person and a second spectroscopic analysis on a second person, andmay generate the first classification model to account for differencesin chemometric signatures (e.g., associated with blood glucose levels)for the first person and the second person (e.g., relating to differingbody composition).

In some implementations, user device 210 may obtain the firstclassification model based on requesting the first classification model.For example, user device 210 may transmit a request for the firstclassification model, and may receive the first classification modelfrom application server 220 based on transmitting the request.Additionally, or alternatively, user device 210 may obtain the firstclassification model from a data structure of user device 210. Forexample, user device 210 may include the first classification modelstored via a data structure. In some implementations, user device 210may generate the first classification model. For example, user device210 may receive a set of chemometric signatures associated with a set ofsubjects, and may generate the first classification model based on theset of chemometric signatures. Additionally, or alternatively, userdevice 210 may perform a set of spectroscopic measurements on a set ofknown subjects to obtain chemometric signatures for the set of knownsubjects, and may generate the first classification model based on thechemometric signatures for the set of known subjects.

As further shown in FIG. 6, process 600 may include obtaining a set ofproperties of a subject for the spectroscopic analysis (block 620). Forexample, user device 210 may obtain the set of properties of the subjectfor spectroscopic analysis. In some implementations, user device 210 maydetermine the set of properties based on sensor data from one or moresensors. For example, user device 210 may utilize a sensor of userdevice 210 to determine a blood glucose level, a body temperature, orthe like regarding a user based on sensor data recorded by the sensor.Additionally, or alternatively, user device 210 may communicate (e.g.,via network 230) with a sensor (e.g., a Bluetooth enabled sensor), toreceive sensor data associated with a property of the subject for thespectroscopic analysis (e.g., the user).

In some implementations, user device 210 may obtain one or moreproperties of the subject associated with categorizing the subject. Forexample, user device 210 may obtain information identifying a gender ofthe subject, an age of the subject, an ethnicity of the subject, or thelike. In this case, user device 210 may obtain the information from adata structure stored by user device 210. Additionally, oralternatively, user device 210 may obtain the information fromapplication server 220 (e.g., an application server associated with ahealth care provider and storing information regarding the user). Insome implementations, user device 210 may obtain the information via auser interface. For example, user device 210 may generate a userinterface and provide a set of prompts for display via the userinterface, and may detect user interactions with the user interfaceassociated with providing a set of responses to the set of prompts.

In some implementations, user device 210 may obtain a property of thesubject of the spectroscopic analysis relating to a raw absorbancespectra of the subject of the spectroscopic analysis. For example, userdevice 210 may utilize an integrated spectroscopic sensor to perform aspectroscopic measurement of a user and determine a chemometricsignature (e.g., a raw absorbance spectra) of the user. Additionally, oralternatively, user device 210 may communicate with a spectroscopicsensor to cause the spectroscopic sensor to determine a chemometricsignature associated with the user, and user device 210 may receive thechemometric signature from the spectroscopic sensor based oncommunicating with the spectroscopic sensor.

As further shown in FIG. 6, process 600 may include generating a secondclassification model for the spectroscopic analysis based on the set ofproperties and the first classification model (block 630). For example,user device 210 may generate the second classification model for thespectroscopic analysis based on the set of properties and the firstclassification model. In some implementations, user device 210 mayutilize a model optimization technique, such as a support vector machineclassifier (SVM) optimization technique, or support vector regression(SVR) optimization technique, or the like, to optimize the firstclassification (or quantitative) model and generate the secondclassification (or quantitative) model. For example, user device 210 mayoptimize the first classification model (e.g., generated based on a setof persons) to generate the second classification model for performingclassification relating to a user of user device 210 (e.g., determininga blood glucose level for the user). In this way, user device 210accounts for differences between persons (e.g., body compositiondifferences).

Additionally, or alternatively, user device 210 may optimize the firstclassification model (e.g., generated based on a first spectrometerassociated with application server 220) to generate a secondclassification model for utilization with a second spectrometerassociated with user device 210. For example, user device 210 maygenerate the second classification model for classifying food itemsbased on spectroscopic sensor data obtained via the second spectrometer.In this way, user device 210 accounts for differences betweenspectrometers, thereby improving accuracy of spectrometry relative toutilizing a single classification model generated by application server220 for each user device 210 utilized by each user.

As further shown in FIG. 6, process 600 may include utilizing the secondclassification model to perform the spectroscopic analysis (block 640).For example, user device 210 may utilize the second classification modelto perform spectroscopic analysis. In some implementations, user device210 may utilize the second classification model to determine a metricassociated with a user (e.g., a subject of the spectroscopic analysis).For example, user device 210 may identify a characteristic (e.g., ablood glucose level, a triglycerides level, a ketone level, an insulinlevel, a skin condition, or a person's identity) based on spectroscopicsensor data associated with the user and the second classification model(e.g., a first chemometric signature, when classified based on thesecond classification model may correspond to a first triglycerideslevel for the user and a second chemometric signature, when classifiedbased on the second classification model may correspond to a secondtriglycerides level. In this case, user device 210 may identify a healthcondition, such as a change to a skin thickness, a change to a skindensity, a change to a skin collagen level, a change to a capillarydensity, or the like, based on detecting the characteristic.

In some implementations, user device 210 may utilize other sensor dataregarding the user to perform the spectroscopic analysis. For example,user device 210 may determine, based on both a chemometric signature ofa user and skin conductivity sensor data regarding the user, that theuser is associated with a particular skin condition. Similarly, userdevice 210 may determine, based on both a chemometric signature of afood item and a volumetric analysis of the food item, a nutritionalcontent of the item (e.g., a calorie content, a carbohydrate content, aprotein content, or a fat content). In this way, user device 210 maycombine sensor data from multiple sensors to determine and providehealth information associated with a user.

In some implementations, user device 210 may provide informationidentifying a health condition based on performing the spectroscopicanalysis. For example, user device 210 may generate a user interface,and may provide information identifying the health condition for displayvia the user interface. Additionally, or alternatively, user device 210may provide information identifying the health condition to applicationserver 220 (e.g., for inclusion in a patient medical record associatedwith the user). Additionally, or alternatively, user device 210 mayprovide information identifying a nutritional content of a food item. Insome implementations, user device 210 may provide the results ofperforming the spectroscopic analysis for further processing. Forexample, user device 210 may perform the spectroscopic analysis, and mayinclude data identifying the spectroscopic analysis in a dataset that isprocessed to determine and provide health information as describedherein with regard to FIG. 4.

As further shown in FIG. 6, process 600 may include periodicallyupdating the second classification model (block 650). For example, userdevice 210 may periodically update the second classification model. Insome implementations, user device 210 may optimize the second model. Forexample, when user device 210 performs a spectroscopic analysis of auser, user device 210 may utilize the spectroscopic analysis and/orother sensor data to refine the second model using an optimizationtechnique, such as an SVM optimization technique or the like. In thiscase, user device 210 may utilize the refined second model forperforming a subsequent spectroscopic analysis. In this way, user device210 improves accuracy of the second classification model relative toutilizing the second classification model without performing arefinement procedure based on spectroscopic analysis and/or other sensordata. Furthermore, over time, the model continues to become moreaccurate and/or more specific to the user of the model, therebypermitting user device 210 to perform more accurate identifications ofcharacteristics of the user, diagnoses of conditions of the user, or thelike. Moreover, based on utilizing the SVM optimization technique, userdevice 210 reduces a utilization of processor and/or memory resources aswell as reducing time intensively relative to generating a newclassification model. Furthermore, an incremental training approach maypermit user device 210 to develop an accurate model as sensor data isreceived by user device 210 rather than requiring sufficient processingand/or memory resources to generate an initial model based on largequantities of sensor data related to increasing quantities of sensorsrelated to the Internet of Things.

As further shown in FIG. 6, process 600 may include periodicallyproviding feedback information associated with causing an update to thefirst classification model (block 660). For example, user device 210 mayprovide feedback information associated with causing an update to thefirst classification model. In some implementations, user device 210 mayprovide information identifying the second classification model to causean update to the first classification model. For example, user device210 may provide information identifying the second classification modelto application server 220 to cause application server 220 to update thefirst classification model.

In some implementations, user device 210 may provide informationidentifying sensor data obtained by user device 210 to cause the updateto the first classification model. For example, when user device 210obtains sensor data indicating a particular body temperature andperforms a spectroscopic measurement of a user with the particular bodytemperature to obtain a chemometric signature for the user at theparticular body temperature, user device 210 may provide informationidentifying the particular body temperature and the chemometricsignature for inclusion in the first classification model (e.g., forutilization in identifying the particular body temperature based on asimilar chemometric signature).

Additionally, or alternatively, user device 210 may provide demographicinformation to update the first classification model. For example, whenthe first classification model is generated based on chemometricsignatures corresponding to blood glucose levels of a group of men, userdevice 210 may determine a chemometric signature corresponding to ablood glucose level for a woman. In this case, user device 210 maytransmit information identifying the chemometric signature for the womento cause the first classification model to be optimized, therebyperforming distributed calibration of the model to improve accuracy ofthe model relative to utilizing a statically calibrated classificationmodel generated based on a limited group of samples (e.g., the group ofmen).

Although FIG. 6 shows example blocks of process 600, in someimplementations, process 600 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 6. Additionally, or alternatively, two or more of theblocks of process 600 may be performed in parallel.

FIGS. 7A and 7B are diagrams of an example implementation 700 relatingto example process 600 shown in FIG. 6. FIGS. 7A and 7B show an exampleof dynamically updating a classification model for spectroscopicanalysis.

As shown in FIG. 7A, and by reference number 705, calibrationapplication server 220 may generate a generic model (e.g., a genericclassification model) based on sample data, obtained via a spectroscopicsensor associated with calibration application server 220, relating to aset of sample persons. As shown by reference number 710, wearable userdevice 210 receives the generic model from calibration applicationserver 220. As shown by reference number 715, wearable user device 210may obtain chemometric signature data relating to a user, such as achemometric signature of a portion of the user's body. As shown byreference number 720, wearable user device 210 may utilize an SVMoptimization technique to generate a local model (e.g., anotherclassification model) for the user based on the chemometric signaturedata, other data, such as sensor data (e.g., a heart rate of the user, askin conductivity of the user, or a blood glucose level of the user),demographic data (e.g., an age of the user, a body mass index of theuser, or a gender of the user), or the like. Assume that wearable userdevice 210 stores the local classification model for utilization inclassifying one or more other chemometric signatures obtained regardingthe user (e.g., for diagnosing a condition of the user).

As shown in FIG. 7B, and by reference number 725, wearable user device210 provides information associated with the local model to update thegeneric model, such as the chemometric signature data, the one or moreother chemometric signatures, the data regarding the user, or the like.As shown by reference number 730, calibration application server 220 iscaused to perform an update of the generic model based on receiving theinformation associated with the local model.

As indicated above, FIGS. 7A and 7B are provided merely as an example.Other examples are possible and may differ from what was described withregard to FIGS. 7A and 7B.

In this way, user device 210 utilizes sensor data from multiple sensorsto provide health information regarding a user, improve calibration ofone or more sensors (e.g., calibration of a classification modelassociated with a spectroscopic sensor), or the like. Moreover, based onutilizing sensor data form multiple sensors, user device 210 reduces acost and power consumption associated with requiring a device for eachsensor. Furthermore, based on utilizing an optimization technique tocalibrate the spectroscopic sensor, user device 210 reduces autilization of processing and/or memory resources relative to generatinga new classification model each time additional sample data and reducescost relative to generating a classification model based on sufficientsample data to perform all classifications without refinement.

The foregoing disclosure provides illustration and description, but isnot intended to be exhaustive or to limit the implementations to theprecise form disclosed. Modifications and variations are possible inlight of the above disclosure or may be acquired from practice of theimplementations.

As used herein, the term component is intended to be broadly construedas hardware, firmware, and/or a combination of hardware and software.

Some implementations are described herein in connection with thresholds.As used herein, satisfying a threshold may refer to a value beinggreater than the threshold, more than the threshold, higher than thethreshold, greater than or equal to the threshold, less than thethreshold, fewer than the threshold, lower than the threshold, less thanor equal to the threshold, equal to the threshold, etc.

Certain user interfaces have been described herein and/or shown in thefigures. A user interface may include a graphical user interface, anon-graphical user interface, a text-based user interface, etc. A userinterface may provide information for display. In some implementations,a user may interact with the information, such as by providing input viaan input component of a device that provides the user interface fordisplay. In some implementations, a user interface may be configurableby a device and/or a user (e.g., a user may change the size of the userinterface, information provided via the user interface, a position ofinformation provided via the user interface, etc.). Additionally, oralternatively, a user interface may be pre-configured to a standardconfiguration, a specific configuration based on a type of device onwhich the user interface is displayed, and/or a set of configurationsbased on capabilities and/or specifications associated with a device onwhich the user interface is displayed.

It will be apparent that systems and/or methods, described herein, maybe implemented in different forms of hardware, firmware, or acombination of hardware and software. The actual specialized controlhardware or software code used to implement these systems and/or methodsis not limiting of the implementations. Thus, the operation and behaviorof the systems and/or methods were described herein without reference tospecific software code—it being understood that software and hardwarecan be designed to implement the systems and/or methods based on thedescription herein.

Even though particular combinations of features are recited in theclaims and/or disclosed in the specification, these combinations are notintended to limit the disclosure of possible implementations. In fact,many of these features may be combined in ways not specifically recitedin the claims and/or disclosed in the specification. Although eachdependent claim listed below may directly depend on only one claim, thedisclosure of possible implementations includes each dependent claim incombination with every other claim in the claim set.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Furthermore,as used herein, the term “set” is intended to include one or more items(e.g., related items, unrelated items, a combination of related items,and unrelated items, etc.), and may be used interchangeably with “one ormore.” Where only one item is intended, the term “one” or similarlanguage is used. Also, as used herein, the terms “has,” “have,”“having,” or the like are intended to be open-ended terms. Further, thephrase “based on” is intended to mean “based, at least in part, on”unless explicitly stated otherwise.

What is claimed is:
 1. A device, comprising: one or more processors to:receive, from a plurality of sensors, sensor data relating to a user,the plurality of sensors including a plurality of types of sensorsincluding a spectrometer and one or more of an accelerometer, a heartrate sensor, a blood pressure sensor, a blood sugar sensor, aperspiration sensor, a skin conductivity sensor, or an imaging sensor;process the sensor data, from the plurality of types of sensors,relating to the user to determine a health condition of the user; andprovide, via a user interface, information identifying the healthcondition of the user based on processing the sensor data, from theplurality of types of sensors, relating to the user.
 2. The device ofclaim 1, where the one or more processors, when processing the sensordata, are to: determine, based on a set of images of a food itemobtained via the imaging sensor, a volume of the food item; determine,based on a chemometric signature of the food item obtained via thespectrometer and based on the volume of the food item, a nutritionalcontent of the food item; and where the one or more processors, whenproviding the information identifying the health condition, are to:providing information identifying the nutritional content of the fooditem.
 3. The device of claim 1, where the one or more processors, whenprocessing the sensor data, are further to: determine an activity levelof the user based on the sensor data from the plurality of sensors, thesensor data including accelerometer sensor data and heart rate sensordata; and where the one or more processors, when providing informationidentifying the health condition of the user, are to: provideinformation identifying the activity level.
 4. The device of claim 1,where the one or more processors are to: receive a diet or exercise planrelating to the user; and where the one or more processors, whenprocessing the sensor data, are to: determine a compliance with the dietor exercise plan by the user based on the sensor data; and where the oneor more processors, when providing information identifying the healthcondition of the user, are to: provide information identifying thecompliance with the diet or exercise plan by the user.
 5. The device ofclaim 1, where the device is a wearable device.
 6. The device of claim5, where the one or more processors, when processing the sensor data,are to: identify a correlation between the user mood and sensor dataassociated with another type of sensor of the plurality of types ofsensors; and where the one or more processors, when providinginformation identifying the health condition, are to: provideinformation identifying the correlation between the user mood and thesensor data associated with the other type of sensor.
 7. The device ofclaim 1, where the one or more processors are further to: generate arecommendation, relating to the health condition of the user, for theuser based on processing the sensor data to determine the healthcondition of the user, the recommendation relating to altering a useractivity level or a user diet to cause a corresponding alteration tosensor data associated with the user activity level or the user diet;and provide information identifying the recommendation.
 8. The device ofclaim 1, where the one or more processors are further to: determine thatthe health condition of the user satisfies a threshold severity; andtransmit an alert to dispatch emergency management personnel to alocation of the user based on determining that the health condition ofthe user satisfies the threshold severity.
 9. The device of claim 1,where the one or more processors, are further to: obtain a firstclassification model relating to performing spectroscopic analysis; andwhere the one or more processors, when processing the sensor data, areto: determine a blood glucose level of the user based on the sensor dataand the first classification model; calibrate the first classificationmodel to generate a second classification model or quantitative modelbased on the first classification model and the blood glucose level,calibrating the first classification model including utilizing anoptimization technique; and utilizing the second classification model orquantitative model to determine another blood glucose level based onother sensor data.
 10. A non-transitory computer-readable medium storinginstructions, the instructions comprising: one or more instructionsthat, when executed by one or more processors, cause the one or moreprocessors to: receive a first spectroscopic classification model, thefirst spectroscopic classification model being associated withidentifying a health condition based on a chemometric signature, thefirst spectroscopic classification model being generated based on acalibration performed utilizing a spectrometer on a group of subjects;obtain a set of properties regarding a user, the set of propertiesincluding first sensor data regarding the user; generate a secondspectroscopic classification model based on the first spectroscopicclassification model and the set of properties regarding the user, thesecond spectroscopic classification model permitting a determination ofa characteristic of the user or a food item; and periodically update thesecond spectroscopic classification model based on second sensor dataregarding the user.
 11. The non-transitory computer-readable medium ofclaim 10, where the one or more instructions, that cause the one or moreprocessors to generate the second spectroscopic classification model,cause the one or more processors to: perform a support vector machineclassifier optimization technique or a support vector regressionclassifier optimization technique on the first spectroscopicclassification model to generate the second spectroscopic classificationmodel; and where the one or more instructions, that cause the one ormore processors to periodically update the second spectroscopicclassification model, are to: perform the support vector machineclassifier optimization technique or the support vector regressionclassifier optimization technique on the second spectroscopicclassification model to update the second spectroscopic classificationmodel.
 12. The non-transitory computer-readable medium of claim 10,where the one or more instructions, when executed by the one or moreprocessors, further cause the one or more processors to: obtain thechemometric signature, the second sensor data including informationidentifying the chemometric signature, the chemometric signature beingdetermined based on sensor data from another spectrometer; perform aclassification of the chemometric signature based on the secondspectroscopic classification model, the classification being associatedwith identifying the health condition; and provide informationidentifying the health condition based on performing the classificationof the chemometric signature.
 13. The non-transitory computer-readablemedium of claim 10, where the one or more instructions, that cause theone or more processors to obtain the set of properties regarding theuser, cause the one or more processors to: obtain the first sensor datafrom a plurality of sensors, the plurality of sensors including at leasttwo of: another spectrometer, an accelerometer, a heart rate sensor, ablood pressure sensor, a blood sugar sensor, a perspiration sensor, askin conductivity sensor, or an imaging sensor.
 14. The non-transitorycomputer-readable medium of claim 10, where the one or moreinstructions, when executed by the one or more processors, further causethe one or more processors to: periodically transmit informationrelating to the second spectroscopic classification model or the secondsensor data to cause an update to the first spectroscopic classificationmodel.
 15. The non-transitory computer-readable medium of claim 10,where the one or more instructions, when executed by the one or moreprocessors, cause the one or more processors to: receive the chemometricsignature from another spectrometer, the other spectrometer beingdifferent from the spectrometer; determine the health condition based onthe second spectroscopic classification model, the health conditionbeing a blood glucose level of the user; and provide informationidentifying the blood glucose level of the user.
 16. The non-transitorycomputer-readable medium of claim 15, where the one or moreinstructions, when executed by the one or more processors, are furtherto: receive another chemometric signature of the user from thespectrometer; detect, based on the second spectroscopic classificationmodel, a threshold difference between the health condition and anotherhealth condition relating to the other chemometric signature; andprovide an alert based on detecting the threshold difference.
 17. Amethod, comprising: determining, by a device, an activity level of auser based on first sensor data relating to the activity level of theuser from a first set of sensors; determining, by the device, anutritional content of a set of food items for consumption by the userbased on second sensor data relating to the nutritional content of theset of food items from a second set of sensors, the second sensor databeing obtained from a spectrometer; obtaining, by the device, a storeddiet and exercise plan for the user, the stored diet and exercise planincluding a goal relating to the activity level of the user and thenutritional content of the set of food items for consumption by theuser; determining, by the device, a user compliance with the stored dietand exercise plan based on the activity level of the user and thenutritional content of the set of food items; and providing, by thedevice, a recommendation relating to user compliance with the storeddiet and exercise plan based on determining the user compliance with thestored diet and exercise plan.
 18. The method of claim 17, furthercomprising: obtaining a generic classification model or quantitativemodel for spectroscopic analysis; generating a local classificationmodel or quantitative model for spectroscopic analysis based on thegeneric classification model or quantitative model and third sensordata, generating the local classification model or quantitative modelincluding utilizing an optimization technique; and where determining thenutritional content of the set of food items comprises: obtaining afirst portion of the second sensor data from an imaging sensor of thesecond set of sensors, the first portion of the second sensor dataincluding a plurality of images; determining a volume of the set of fooditems based on the plurality of images; obtaining a second portion ofthe second sensor data from the spectrometer, the second portion of thesecond sensor data including a chemometric signature; performing aclassification of the set of food items based on the chemometricsignature and the local classification model or quantitative model; andidentifying the nutritional content of the set of food items based onthe volume of the set of food items and the classification of the set offood items.
 19. The method of claim 17, where determining the activitylevel further comprises: determining a calorie expenditure by the userbased on the first sensor data; and where determining the usercompliance with the stored diet and exercise plan comprises: determiningthat a calorie intake by the user associated with consumption of the setof food items exceeds the calorie expenditure by a threshold amount, thecalorie intake being related to the nutritional content of the set offood items.
 20. The method of claim 19, further comprising: determiningan amount of exercise that corresponds to the threshold amount; andwhere providing the recommendation comprises: providing informationidentifying the amount of exercise that corresponds to the thresholdamount.